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de Haan, Jakob; Sturm, Jan-Egbert

Working Paper

Finance and Income Inequality: A Review and NewEvidence

CESifo Working Paper, No. 6079

Provided in Cooperation with:Ifo Institute – Leibniz Institute for Economic Research at the University of Munich

Suggested Citation: de Haan, Jakob; Sturm, Jan-Egbert (2016) : Finance and IncomeInequality: A Review and New Evidence, CESifo Working Paper, No. 6079, Center for EconomicStudies and ifo Institute (CESifo), Munich

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Finance and Income Inequality: A Review and New Evidence

Jakob de Haan Jan-Egbert Sturm

CESIFO WORKING PAPER NO. 6079 CATEGORY 7: MONETARY POLICY AND INTERNATIONAL FINANCE

SEPTEMBER 2016

An electronic version of the paper may be downloaded • from the SSRN website: www.SSRN.com • from the RePEc website: www.RePEc.org

• from the CESifo website: Twww.CESifo-group.org/wp T

ISSN 2364-1428

CESifo Working Paper No. 6079

Finance and Income Inequality: A Review and New Evidence

Abstract Using a panel fixed effects model for a sample of 121 countries covering 1975‐2005, we examine how financial development, financial liberalization and banking crises are related to income inequality. In contrast with most previous work, our results suggest that all finance variables increase income inequality. The level of financial development conditions the impact of financial liberalization on inequality. Also the quality of political institutions conditions the impact of financial liberalization on income inequality, in contrast to the quality of economic institutions. Our main findings are robust for using random effects, cross‐country regressions and legal origin as instrument for financial development.

JEL-Codes: D310, D630, F020, O110, O150.

Keywords: income inequality, financial liberalization, financial sector size, financial crises, political institutions.

Jakob de Haan

Department of Economics University of Groningen

P.O.Box 800 The Netherlands - G700 Groningen AV

jakob.de.haan@rug.nl

Jan-Egbert Sturm KOF Swiss Economic Institute

ETH Zurich LEE G 116, Leonhardstrasse 21

Switzerland - 8092 Zurich sturm@kof.ethz.ch

Version: September 2016

2

1. Introduction

We examine the relationship between finance and income inequality using panel fixed

effects regressions for a large sample of countries. To bemore precise, we analyze how

financial development, financial liberalization and financial crises are related to within

country income inequality. As dependent variable we use five‐year averages of Gini

coefficients based on households’ gross income from Solt’s (2009) Standardized World

IncomeInequalityDatabase(SWIID).

There isanextensive literatureon the relationshipbetween financialdevelopment

andincomeinequality.1Theoretically,theimpactoffinancialdevelopmentisambiguous.On

the onehand,more financemaymake it easier for poor individuals toborrow for viable

projects, which may reduce income inequality (Galor and Moav, 2004). Financial

imperfections,suchasinformationandtransactioncosts,maybeespeciallybindingonthe

poorwho lackcollateralandcredithistories so that relaxationof thesecredit constraints

maybenefit thepoor (Beck et al., 2007).On theother hand, improvements in the formal

financial sector could be more likely to benefit the well‐off who rely less on informal

connections for capital (Greenwood and Jovanovic, 1990). As will be discussed in more

detail in section 2, the empirical evidence on the relationship between financial

developmentand income inequality isverymixed. Insteadofprovidingyetanothersetof

regressions that possibly adds to thisdiversity,we examinewhether institutional quality

conditions the relationship between financial development and income inequality, which

mayshedsomelightonthereasonswhystudiesreachdifferentconclusions.Accordingto

Rajan andZingales (2003), inweak institutional environments established interests have

privileged access to finance so that financial development induced by captured direct

controls is likely to hurt the poor. In the presence of strong institutions, financial

development may reduce inequality, allowing the poor to invest in human and physical

capital(Lawetal.,2014).Asimilarargumentcanbemadeforfinancialliberalization(Delis

etal.,2014).2

1SeeClaessensandPerotti(2007)andDemirgüç‐KuntandLevine(2009)forexcellentreviewsoftheliterature.

2Delisetal.(2014,p.1824)arguethat“qualityinstitutionsmightenhancetheimpactofregulationson the distribution of income andweaker institutionsmay undermine such an impact.” However,theydonotexaminethis.Thepresentstudythereforeisthefirsttoexaminewhethertheimpactoffinancialliberalizationonincomeinequalityisconditionedbyinstitutionalquality.

3

Inrecentdecadestherehasbeenaglobalpushtoliberalizethefinancialsector. A

small, but growing line of literature examines the impact of financial liberalization on

income inequality. For instance, Beck et al. (2010) assess the impact of U.S. bank

deregulation of the 1970s to the 1990s on the distribution of income and find that

deregulationsignificantlyreduces inequalitybyboosting incomes inthe lowerpartof the

income distribution but has little impact on incomes above the median. Likewise, some

recent studies (Agnello et al., 2012; Delis et al., 2014; Li and Yu, 2014) based on cross‐

country data report that financial liberalization reduces income inequality, but Jaumotte

and Osuorio Buitron (2015) and Naceur and Zhang (2016) conclude that financial

liberalization increases inequality (see section 2 for more details). Bumann and Lensink

(2016) suggest that the impact of financial liberalization on inequality is conditioned by

financial development. In their view, “financial liberalization will improve income

distribution incountrieswherefinancialdepth ishigh.Themainreasonforourfinding is

that in countrieswith high financial depth, the interest rate elasticity of loan demand is

high.Afinancialliberalizationpolicythatimprovesbankefficiencyandreducesborrowing

costswill lead toa sharp increase inaggregate loandemand,requiringan increase in the

depositratetorestoreequilibriuminthefinancialmarket.Theincreaseinthedepositrate

improvestheincomeofsaversand,hence,incomedistribution”(p.144).3Wewillexamine

whetherfinancialdevelopmentconditionsthe impactof financial liberalizationon income

inequality.

Athirdfinancialvariablethatweconsiderarefinancialcrises.Conventionalwisdom

is that the poor suffer disproportionately from recessions following financial crises.

However, Denk and Cournede (2015) do not find a significant effect of banking crisis in

theiranalysisofincomeinequalityinOECDcountries.Onlyfewstudies(e.g.Baldaccietal.,

2002; Agnello and Sousa, 2012 and Li and Yu, 2015) analyze the causal relationship

between financial crises and income inequality for a broader set of countries and report

mixedfindings.

Our contribution to the literature is threefold. First, we include financial

development, financial liberalization and financial crises in our empirical analysis of the

relationshipbetweenfinanceandincomeinequality.Previousstudiesincludeatbesttwoof

3In their model, agents with the best investment skills become investors, and earn the highestamountofmoneyandagentswithfewerinvestmentskillsbecomesavers,andearnlessmoney.

4

these variables at the same time. Second, we use different indicators of financial

liberalization.Likepreviousstudiesweuse the financial liberalizationdataofAbiadetal.

(2010), but also construct an alternative indicator based on some components of the

economicfreedomindexoftheFraserInstitute(Gwartneyetal.,2015).Thisenablesusto

checkhowsensitiveresultsareforthewayfinancial liberalizationismeasured.Third,we

examinewhethertheimpactoffinanceonincomeinequalityisconditionedby:(1)thelevel

offinancialdevelopmentassuggestedbyBumannandLensink(2016);and(2)institutional

qualityassuggestedbyRajanandZingales(2003).

Our results suggest that a higher level of financial development, financial

liberalization and the occurrence of a banking crisis all increase income inequality in a

country. Incontrast to thepredictionbyBumannandLensink(2016),ourresultssuggest

that with high levels of financial development, financial liberalization increases income

inequality. We also find evidence that, unlike the quality of economic institutions, the

quality of political institutions conditions the impact of financial liberalization on income

inequality: with higher levels of democratic accountability the positive effect of financial

liberalizationoninequalityincreases.Institutionalqualitydoesnotconditiontheimpactof

financial development on income inequality, in contrast to the prediction by Rajan and

Zingales(2003).

The remainder of the paper is structured as follows. Section 2 discusses related

studiesinmoredetail.Section3describesourmethodologyanddataused,whilesection4

presentsthemainresults.Section5offersasensitivityanalysisandsection6concludes.

2. Literature review

As pointed out by Demirgüç‐Kunt and Levine (2009), theory provides ambiguous

predictions for the impact of finance on income distribution. A distinction can be made

between the effects of finance on the extensive and the intensivemargin. The extensive

margin isabout theuseof financialservicesby individualswhohadnotbeenusing those

services.Forexample, financialdevelopmentmayhelppoor families toborrowtopay for

education. Inequality falls in models with this mechanism (Galor and Moav, 2004).4The

4However, thequestion iswhether financialdevelopmentassuchreducesthese financial frictions.Perhapsthesefrictionscanbereducedbyotherfactors,suchastechnology,withoutalargerfinancialsector (Demirgüç‐Kunt and Levine, 2009). This suggests that other financial sector characteristics

5

effect of financial development on income inequality on the intensivemargin is different.

Improvementsinthequalityandrangeoffinancialservicesdonottendtobroadenaccess

tofinancialservices,butinsteadimprovethequalityoffinancialservicesenjoyedbythose

already purchasing financial services (Greenwood and Jovanovic, 1990). The benefits of

these intensive margin effects accrue primarily to the rich, widening the distribution of

income.

Theextensiveempiricalliteratureontherelationshipbetweenfinancialdevelopment

andincomeinequality provides verymixed findings.5Although several studies report that

countrieswithhigherlevelsoffinancialdevelopmenthavelessincomeinequality(seee.g.Li

etal.1998,Clarkeetal.,2006,Becketal.,2007,Kappel,2010,HamoriandHashiguchi,2012

andNaceurandZhang,2016),6otherstudiesreportanon‐linearrelationship(e.g.Kimand

Lin,2011andLawetal.,2014),7mixedresults(Bahmani‐OskooeeandZhang,2015),8ora

thansizeshouldbeexamined.Mostempiricalresearchfocuses,however,on financialsectorsize,arecentexceptionbeingthestudybyNaceurandZhang(2016).

5Herewe only discuss research usingmacro data for a large set of countries. For a discussion ofothertypesofresearchwerefertoDemirgüç‐KuntandLevine(2009).

6Lietal.(1998)usedatafor49countriesoverthe1947‐94periodandreportastrongrelationshipbetween income inequality and their measure for financial development (M2/GDP). Beck et al.(2007)reportanegativerelationshipbetweenfinancialdevelopment(proxiedbyprivatecredit‐to‐GDP)andthegrowthrateoftheGinicoefficient,whichholdswhencontrollingforrealpercapitaGDPgrowth,laggedvaluesoftheGinicoefficient,andawidearrayofothercountry‐specificfactors.Theirsampleconsistsof65countriesovertheperiod1960‐2005.Usingasimilarmodelforalargergroupof countries (83) but a shorter sample period (1960‐1995), Clarke et al. (2006) also find thatfinancialdevelopment reduces inequality.Kappel (2010),whousesa sampleof59 countries for across‐countryanalysisand78countriesforapanelanalysisovertheperiod1960to2006,concludesthat financial development reduces income inequality for high‐income countries, but is notsignificant for low‐income countries. Hamori and Hashiguchi (2012) use annual panel data for asampleof126countriesoverthe1963‐2002periodandfindthatbothM2/GDPandprivatecredit‐to‐GDP reduce estimated household income inequality when they use panel fixed effects and GMM.Naceur andZhang (2016)use a sample of143 countries from1961 to2011 and find that severaldimensions of financial development considered (access, efficiency, deepening and stability) cansignificantlyreduceincomeinequalityandpoverty,whilefinancialliberalizationtendstoexacerbatethem.

7Based on a sample of 65 countries for 1960‐2005, Kim and Lin (2011) find that the benefits offinancialdevelopmentonincomedistributionoccuronlywhenthecountryhasreachedathresholdlevel of financial development. Below this critical threshold, financial development exacerbatesincomeinequality.Usingdatafor81countriesovertheperiod1985‐2010inacross‐sectionmodel,Lawetal.(2014)concludethatfinancialdevelopmenttendstoreduceincomeinequalityonlyafteracertain threshold level of institutional quality hasbeen achieved.Until then, the effect of financialdevelopmentonincomeinequalityisnonexistent.

8Usingtimeseriesregressionsfor17countries,Bahmani‐OskooeeandZhang(2015)reportthatonlyin three out of the 10 countries where finance has a short‐term equalizing effect on income

6

positive relationship between financial development and income equality. For instance,

JauchandWatzka(2012),whouseapanelof138countriesfortheyears1960‐2008,find

that financial development increases income inequality when they use fixed effects and

controlforGDPpercapita.Jaumotteetal.(2013)investigateincomeinequalitywithafocus

ontradeandfinancialglobalization.Intheirsampleof51countriesfrom1981to2003,they

includeprivatecreditoverGDPasacontrolvariableandobtainapositiveandsignificant

coefficient for financial development. Similar results are reported by Dabla‐Norris et al.

(2015),whoconcludethatfinancialdeepeningisoneofthemaindriversoftheincreasein

income inequality in their sample covering 97 countries and five‐year panels over the

period1980–2012.

In thepanel regressions for theGini coefficient ina sampleof18Asiancountries

overthe1996‐2005reportedbyLiandYu(2015)thecoefficientofcredit‐to‐GDPispositive

and significant. Likewise, Denk and Cournède (2015) conclude that more finance is

associated with higher income inequality in their sample of 33 OECD countries. This

relationshipholdswhen intermediatedcredit andstockmarket capitalizationareused to

measure thesizeof finance.Financial sectoremployeesareverystronglyconcentratedat

the top of the income distribution, and their earnings exceed those of employees with

similarprofiles(suchasage,genderoreducation)inothersectors(Denk,2015).

Whereasmost studies discussed do not explore the transmission from finance to

inequality, Gimet and Lagoarde‐Segot (2011) examine specific channels linking banks,

capitalmarketsandincomeinequality.Theyconstructasetofannualindicatorsofbanking

and capital market size, robustness, efficiency and international integration and then

estimate the determinants of income distribution using a panel structural vector

autoregressivemodelfor49countriesoverthe1994–2002period.9Theseauthorsconclude

thatfinancialsectordevelopmentincreasesincomeinequalityandthatthisimpactseemsto

runprimarilyviathebankingsector;NaceurandZhang(2016)reachthesameconclusion.

Finally, some studies suggest that the impact of financial development on income

inequalitymaybeconditionedbythequalityofinstitutions(cf.Delisetal.,2014andLawet

distributiontheimprovementlastsinthelongrun.

9Inviewof thequalityand frequencyofdataon income inequality,wehave seriousdoubtsaboutusingannualdataonincomeinequality.ThiscritiquealsoappliestootherstudiesusingannualdatasuchasLiandYu(2014),Bahmani-Oskooee

and Zhang (2015) and Naceur and Zhang (2016).

7

al., 2014). For instance, under lowquality of economic institutions financialdevelopment

(or financial liberalization)maynot affect inequalitydue to lackof judicialprotection for

thepoor(ChongandGradstein2007).Likewise,RajanandZingales(2003)arguethatunder

weakpoliticalinstitutionsdejurepoliticalrepresentationisdominatedbydefactopolitical

influenceallowingestablished interests to influenceaccess to financeso that theybenefit

morefromfinancialdevelopmentthanthepoor.

Severalargumentshavebeenputforwardintheliteraturesuggestingthatfinancial

sector liberalization may affect income distribution. First, imperfections in the credit

market prevent the poor frommaking productive investment, in for instance, education

(Banerjee and Newman, 1991). If financial liberalization reduces these credit market

imperfections, income inequality may be reduced. Second, financial reformsmay lead to

more equal access to credit thereby improving the efficiency of the domestic financial

system(Abiadetal.,2008).

A few studies examine the relationshipbetween financial sector liberalizationand

incomeinequalityusingcross‐countrydata(mostofthesestudiesusethedatabaseofAbiad

et al. (2010) for measuring liberalization; see section 3 for further details). Das and

Mohapatra (2003) find that liberalization of equity markets benefits people in the top

quintileoftheincomedistributionattheexpenseofthe‘middleclass’,whilepeopleinthe

lowestincomesharesarenotaffected.Usingapanelof62countriesfor1973–2005,Agnello

etal. (2012)analyzethe impactof financialreformson income inequality.Theirevidence

suggests that removal of policies towards directed credit and excessively high reserve

requirements, and improvements in the securities market reduce income inequality.

Likewise,Delisetal.(2014)concludethathigherliberalizationofbankinggenerallyleadsto

narrower incomedistribution.Yet, theyalso find that this effect is not uniformacross all

liberalization policies, nor is it the same across countries with different levels of

developmentordifferenttypesoffinancialenvironments.Inparticular,theabolishmentof

creditcontrolsdecreases income inequalitysubstantially,andthiseffect is long lasting.Li

and Yu (2014) report for 18 countries in Asia for the 1996‐2005 period that financial

reformiseffectiveinreducingincomeinequality,butthattheeffect ismoreprofoundina

country with higher human capital. Jaumotte and Osuorio Buitron (2015) investigate

income inequality in 20 advanced economies during 1980–2010 with a focus on labor

8

market institutions and include the indexofAbiad et al. (2010) as control variable. They

findthatitscoefficientissignificantlypositive.10AlsoNaceurandZhang(2016)reportthat

financialliberalizationincreasesinequality.

ChristopoulosandMcAdam(2015)examinethelinkbetweenfinancialreformsand

thestabilizationof incomeinequalityusingpanelunitroottestsextendedtoallowforthe

presence of some covariates. Their results suggest that although both gross and net Gini

indicesfollowaunitrootprocessthispicturechangeswhenthevariousfinancialreforms

indicesareconsideredasadditionalcovariatesinthestandardpanelunitrootapproach.In

particularwhilstgrossGinicoefficientsaregenerallynotstabilizedbyfinancialreforms,net

measuresaremorelikelytobestabilized.

Bumann and Lensink (2016) argue that the impact of financial liberalization on

inequality is conditioned by financial development. Their theoreticalmodel suggests that

financialliberalizationwillimproveincomedistributionincountrieswherefinancialdepth

ishigh.Theauthors’empiricalresultssuggestthattheirmeasureoffinancialliberalization,

i.e. capital account liberalization, only tends to lower income inequality if the level of

financialdepth,asmeasuredbyprivatecreditoverGDP,exceeds25percent.Theseresults

stand incontrast to the findingsofFurceriandLoungani (2015).Basedonpaneldata for

149countriesfrom1970to2010,theseauthorsconcludethat,onaverage,capitalaccount

liberalization increases inequality. In addition, their results suggest that capital account

liberalization leads to larger increases in inequality in countries with a weak level of

financialinstitutionsandwhentheyarefollowedbyepisodesoffinancialcrises.

Finally,weconsidertheimpactoffinancialcrisesonincomeinequality.Wealthlosses

duetoafinancialcrisisprobablywillhitthetopoftheincomedistribution.However,low‐

income individuals will be hit more if the financial crisis is followed by an economic

downturn(whichisnotalwaysthecase).Indeed,accordingtotheOECD(2013),duringthe

global financial crisis the average market income inequality across OECD countries

increasedby1.4percentagepoints. Looking at the17OECDcountries forwhichdataare

available over a long time period,market income inequality increased bymore between

10ThisfindingisconsistentwiththeresultsreportedbyPhillipponandReshef(2013)whoexaminelong‐run trends in finance in a few advanced economies. They find that financial deregulationincreasedthedemandforskillsinthefinancialsectorandthatrelativewagesinthefinancialsectorarerelatedtoskill‐intensity.

9

2007 and 2010 than what was observed in the previous 12 years. However, Denk and

Cournede(2015)donotfindasignificanteffectofbankingcrisiscrisesintheiranalysisof

income inequality in 33OECD countries during 1970‐2011. As far aswe know, only few

studies have examined the causal relationship between financial crises and income

inequality forabroadersetofcountries.Baldaccietal.(2002)reportthatcurrencycrises

haveapositiveimpactontheGinicoefficient.IntheiranalysisofincomeinequalityinAsian

countries,LiandYu(2015)includeabankingcrisisdummyandfindthat ithasapositive

relationshipwith the Gini coefficient (crises lead tomore inequality). Also Atkinson and

Morelli (2011) find that income inequality is likely to increase after a banking crisis. In

contrast, Agnello and Sousa (2012), who use annual data for 62 OECD and non‐OECD

countriesforthe1980‐2006periodfindmixedresults.WhileforOECDcountriesabanking

crisisreducesinequality,fornon‐OECDtheauthorsobserveasignificantriseininequality

beforetheonsetofthecrisisbutnoeffectthereafter.Incontrast,forasampleofdeveloping

countries,Honohan(2005)doesnotfindevidenceforasignificantdifferencebetweenGini

coefficients before and after a banking crisis. Likewise, Jaumotte and Osuorio Buitron

(2015)donotreportasignificantimpactofbankingcrisesonincomeinequality.

Whilethereislimitedresearchonacausalrelationshipbetweenfinancialcrisisand

inequality,thecausalityintheotherdirection,i.e.from(increasesin)incomeinequalityto

financial crises, has received substantial attention. High or rising income inequality may

cause low‐incomegroupsto leverage inorderto increaseormaintainconsumption levels

which,inturn,mayincreasethelikelihoodofafinancialcrisis.Therelativeincometheory,

habit formations and a ''keeping up with the Joneses'' phenomenon may explain such

behavior (see Atkinson and Morelli, 2011 for a further discussion). For instance, in the

modelofKumhofandRancière(2011)risingincomeinequalityandstagnantincomesinthe

lower deciles lead workers to borrow to maintain consumption growth. This increases

leverage,andeventuallyashocktotheeconomyleadstoafinancialcrisis.Indeed,thereis

much evidence that financial crises are often preceded by credit booms (Schularick and

Taylor,2012).

However,theempiricalevidenceinsupportofcausalityrunningfrominequalityto

financial crises isweak at best. Cross‐country data indicate that banking crises have not

systematicallybeenprecededbyrising inequality(AtkinsonandMorelli,2011;Bordoand

10

Meissner,2012),althoughGuandHuang(2014)reportsomesupportingevidence.11

3. Data and method

3.1Incomeinequality

Ourleft‐handsidevariable istheGinicoefficientbasedonhouseholds’ incomefromSolt’s

(2009) StandardizedWorld Income Inequality Database (SWIID). We use the index that

represents household income before taxes, as this shows inequality exclusive of fiscal

policy.12As pointed out byDelis et al. (2014) and Solt (2015), the SWIIDdatabase is the

most comprehensive database and allows comparison across countries, because it

standardizes income.13The Gini coefficient is derived from the Lorenz curve and ranges

between 0 (perfect equality) and 100 (perfect inequality).We acknowledge that the Gini

coefficientislessthanperfectandthatothermeasures,suchastheshareofincomeofthe

lowest quintile,may sometimes bemore appropriate.Data availability, however, dictates

our choice.We construct averages of the Gini coefficients across 5 yearswhere the Gini

coefficientsarecenteredatthemiddleofthefive‐yearperiod.

Weusefive‐yearnon‐overlappingaveragesforthreereasons(seealsoDabla‐Norris

etal.,2015).First,annualmacroeconomicdataarenoisy,andthisappliesespeciallyfordata

on income inequality (Delis et al., 2014). Second, the annual income inequality data in

SWIID are imputed for years for which no information was available in the underlying

databases (there are only infrequent measures of inequality for much of Africa, Latin

America,andAsia).Third,someoftheexplanatoryvariablesusedareonlyavailableforfive‐

11AtkinsonandMorelli(2011)examinetherelationshipbetweencrisesandincomeinequalityusingcase studiesofbankingcrisesovera100‐yearperiod (1911‐2010) in25countries.Theyconcludethat “bankingcriseswereprecededby falling inequality asmany timesasby rising inequality” (p.47). They also report that there “is more evidence that financial crises are followed by risinginequality” (p. 49). Using data from 14 advanced countries between 1920 and 2000, Bordo andMeissner(2012)reportthatcreditboomsheightentheprobabilityofabankingcrisis,butthereisnoevidencethatariseintopincomesharesleadstocreditbooms.GuandHuang(2014)challengetheseresults on econometric grounds.Using a similar dataset, they “establish strongevidence for risinginequalityasasignificantdeterminantofcreditboomsandthereforefinancialcrisesinAnglo‐Saxoncountriesandothersimilareconomies”(p.513).However, forothercountriestheirevidenceisnotsupportiveforapositivecausallinkfrominequalitytocrises.

12Using Gini coefficients for net income, as some studies do (e.g. Agnello et al., 2012) wouldcomplicateidentificationoftheeffectoffinanceonincomeinequality.

13Still,itisnotwithoutproblems;seeGalbraith(2012;chapter2)foranextensivediscussion.

11

year intervals. Fourth, we are not so much interested in short‐term, i.e. business cycle,

driveneffects.

WehaveconsideredusingtheWorldIncomeInequalityDatabase(WIID)insteadof

SWIID as source for data on income inequality. SWIID is based on WIID, but is

supplementedbyother sources andhas allof itsobservationsmultiply‐imputed (Jenkins,

2015).14WIIDoftenprovidesmorethanoneGinicoefficientforthesamecountry/year.To

dealwiththisproblem,weproceedasfollows.Wefirsttakeaveragesofcountry/yearpairs

thathavethesamequalitylabel(high,average,low,notknown).Thisreducesthenumber

ofpotentialduplicatestoatmost4(high,average,low,notknown)percountry/yearpair.

Next,wetaketheaverageGinicoefficientthatbelongstothehighestqualitygroupsothat

wehaveoneobservationpercountry/year,whichcanbeanaverageofseveralobservations

ofthehighestqualityavailable.Aswearenotinterestedinshort‐rundynamics,ouranalysis

isbasedon5‐yearaverages.AsSWIIDprovidesobservationsforeachyear,theseaverages

canbeeasilycomputed.InWIIDtherearemanymissingobservations.Weconsideredtwo

alternative approaches to construct proxies for theGini coefficientmeasuredover5‐year

intervals.First,wetaketheGinicoefficientsinthemiddleofthis5‐yearperiod,ifavailable

(Gini(WIID)).Thedownsideofthisapproachisthatwearenotaveragingoutofacoupleof

adjacentyears.Asanalternative,wetakea5‐yearsaverage(Gini5yearsavg(WIID));this

results in a non available in case at least one of these years is not available, thereby

reducingthesamplesubstantially.

[Table1here]

Table1presentssomesummarystatisticsforthegrossandnetGinicoefficientsdrawn

fromSWIIDandtheGinicoefficientsconstructedonthebasisofWIID.Asthetableshows,

thenumberofobservationsavailablefortheincomeinequalitymeasuresbasedonWIIDis

much lower than for those based on SWID. When using data from WIID we cannot

distinguishbetweenmarketandnetGinicoefficients.AspointedoutbySolt(2015,p.685)

thisisproblematic:“mixinggross‐anddisposable‐incomeobservations…suggestsasimple

failuretoconsiderwhatisthetheoreticallyrelevantvariable…”However,suchadistinction

14WethankStephenJenkinsforprovidingthisdatabase.

12

wouldreducethesampleevenfurther.Itappearsthataboutaquarteroftheobservationsin

WIIDfollowsomekindofgrossincomeconcept.

The correlations reported in Table 1 support this and suggest thatmost of theWIID

dataareindeedbasedonanetincomeconcept:thecorrelationoftheWIIDvariableswith

our(preferred)grossGinicoefficientfromSWIIDisrelativelylow(0.6or0.5),whereasitis

muchhigherwiththenetGinicoefficientsfromSWIID(around0.9).

As we are interested in the impact of finance on income inequality before income

redistributionanddowanttobeabletoworkwitharespectableandrepresentativesample,

wedecidedagainstusingtheWIIDdata.

3.2Explanatoryvariables

WemeasurefinancialdevelopmentbyprivatecreditdividedbyGDP.Thismeasureexcludes

credit to the central bank, development banks, the public sector, credit to state‐owned

enterprises,andcrossclaimsofonegroupof intermediariesonanother.Thus, itcaptures

the amount of credit channeled from savers, through financial intermediaries, to private

firms. It has advantages over alternativemeasures of financial development, such asM2

overGDP,whichdoesnotmeasureakeyfunctionoffinancial intermediaries,whichisthe

channelingofsociety’ssavingstoprivatesectorprojects(Becketal.,2007).Inaddition,the

evidenceofGimetandLagoarde‐Segot(2011)andNaceurandZhang(2016)suggeststhat

theimpactoffinanceonincomeinequalityrunsviathebankingsectorratherthancapital

marketcapitalization.15

Figure1showstwoscatterplotsofourmeasuresforincomeinequalityandfinancial

development.The graphon the left‐hand side shows the relationshipusing the rawdata.

This graph does not suggest that there is a relationship between the two variables. The

graph on the right‐hand side shows the relationship controlling for country‐fixed effects.

Thisgraphsuggeststhatmorefinancialdevelopmentincreasesincomeinequality.

[Figure1here]

15Using the data as described in Čihák et al. (2012), we also investigate whether the results aresensitive to using stock market capitalization as percentage of GDP as measure of financialdevelopment.Althoughthisisreducingthesamplesubstantially,thequalitativeresultstendtogointhesamedirection.

13

We use twomeasures for financial sector liberalization. First, following previous

studies we employ the data of Abiad et al. (2010) that is based on several sub‐indices

mostlypertaining tobanking regulatorypracticesmeasuredon a scale from0 to3 (fully

repressed to fully liberalized). The database covers 91 economies over the 1973–2005

periodandconsistsofseven indicesof financialsector liberalization.Our firstmeasureof

financial liberalization is the sum of six sub‐indices. As the sub‐index on banking

supervisionisnotaboutfinancialsectorliberalizationweexcludeit.Oursampleforwhich

weusethisproxyforfinancialliberalizationconsistsof89countries(listedinTableA1of

theAppendix)andrunsfrom1975to2005.

Asanalternative,weemploydata from theFraser Instituteoneconomic freedom

thathasabroadercoverageofthefinancialsector(aspointedoutbyDelisetal.(2014),the

indexofAbiadetal.(2010)primarilyreflectspoliciesrelatedtothebankingsector)16andis

availableformorecountriesandmorerecentyears.Theeconomicfreedomindexcoversup

to 157 countries with data relevant for this paper being available for approximately 70

countries as far back as 1975.17We use the sum of four sub‐indices from the economic

freedomdatabase,namelythesub‐indices3D,4C,4Dand5A.Theseindicesrangebetween

0(not free) to10(totally free).Thefirst indexrefers to freedomtoownforeigncurrency

bankaccountsandmeasurestheeasewithwhichothercurrenciescanbeusedviadomestic

andforeignbankaccounts.Thesecondindexisbasedonthepercentagedifferencebetween

the official and the parallel (black) market exchange rate. Countries with a domestic

currencythatisfullyconvertiblewithoutrestrictionsreceiveascoreoften.Whenexchange

ratecontrolsarepresentandablackmarketexists,theratingswilldeclinetowardzeroas

theblack‐marketpremiumincreasestowardmorethan50%.Inthelattercase,azerorating

isgiven.The third indexmeasurescontrolsof themovementof capital. The fourth index

measurestheextenttowhichthebankingindustryisprivatelyowned,theextenttowhich

creditissuppliedtothegovernmentsectorandwhethercontrolsoninterestratesinterfere

withthemarketincredit.Oursampleforwhichweusethisproxyforfinancialliberalization

16The sub‐indices of the index of Abiad et al. (2010) refer to credit controls and reserverequirements, interest rate controls, banking‐sector entry, capital‐account transactions,privatizations of banks, liberalization of securities markets, and banking‐sector supervision andcapitalregulation.

17The data go back to 1970 but cover only 53 countries. Several studies have examined therelationshipbetweentheoveralleconomicfreedomindexandincomeinequality,see,e.g.BerghandNilsson(2010)andSturmanddeHaan(2015).

14

consistsof121countries(listedinTableA1oftheAppendix)andrunsfrom1975to2005.

Figures2and3showtherelationshipbetweenourmeasuresforincomeinequality

andfinancialliberalization,againwithandwithoutcontrollingforfixedeffects.Thegraphs

withoutfixedeffectsdonotsuggestthatthereisarelationshipbetweenincomeinequality

and financial liberalization, while those with fixed effects suggest that financial

liberalizationleadstomoreinequality.

[Figures2and3here]

OurcrisisdatacomefromLaevenandValencia(2013)whoprovideinformationon

the timing of systemic banking crises. Chaudron and de Haan (2014) show that this

database ismore reliable than competing financial crises databases. Crises are identified

based on several criteria. First, there should be signs of financial distress in the banking

system. Banking crises are also identified by “significant banking policy intervention

measures”ofwhichtheyidentifysix(suchasadepositfreezeornationalizations).Atleast

three of these measures need to have been implemented for a crisis to be classified as

systemic.Thisconditionissupplementedwiththreeothercriteria,namelythattheshareof

nonperforming loans exceed 20 percent, bank closures make up at least 20 percent of

bankingassetsandfiscalrestructuringcostsexceed5percentofGDP.Ourcrisisvariableis

onewhenabankingcrisisstartedinthefive‐yearperiodbeforeandiszerootherwise.

3.3Method

As we are interested in the within country relationship between finance and income

inequality,weuseadynamicpanelmodel insteadofOLScross‐sectionregressions inour

main analysis. As pointed out by Beck et al. (2007), a dynamic panelmodel has several

advantages compared to cross‐country regressions as the latter do not fully control for

unobserved country‐specific effects and do not exploit the time‐series dimension of the

data.Themodelestimatedis:

, , , , , ,

WhereIneq isincomeinequality,FD isfinancialdevelopment,FLisfinancialliberalization,

BCdenotestheoccurrenceofabankingcrisisandXisavectorofcontrolvariables,whileu

denotestheerrorterm.Timelagsareusedtoavoidendogeneityissues(butthismaynotbe

15

sufficientandthereforeweconsideralternativeapproachesbelow).ForFDandFLwetake

valuesattheendofthefive‐yearperiodprecedingtheperiodcoveredbytheGinicoefficient

(whichisafive‐yearaverage),whilethebankingcrisisdummyisonewhenabankingcrisis

startedinanyofthefiveyearsprecedingthefive‐yearperiodusedforcalculatingtheGini

coefficient.We have used a very long list of control variables based on previous studies

(showninTableA2intheAppendix;TablesA3andA4providessummarystatisticsanda

correlationmatrix).18

AspointedoutintheIntroduction,wefocusontwointeractionsthat,accordingto

insightsfromtheliterature,mayconditiontheimpactoffinanceonincomeinequality.First,

weexaminewhethertheimpactoffinancialliberalizationonincomeinequalitydependson

the level of financial sector development. Second, we examine whether the impact of

financialliberalizationand/orfinancialdevelopmentonincomeinequalityisconditionedby

institutionalquality.

We have constructed two institutional quality variables using the ICRG database

measuring the quality of political institutions and the quality of economic institutions,

respectively.Onascalefromzero(lowquality)tosix(highquality),thevariabledemocratic

accountabilitymeasures not just whether there are free and fair elections, but also how

responsive government is to its people. This variable comes directly from the ICRG

database. It measures precisely what e.g. Acemoglu and Robinson (2013, p. 36) have in

mindwhen theyexplainwhy thequalityofpolitical institutionsmatters inexplaining the

different economic fates of Mexico and the US and the role of access to finance therein:

“Unlike inMexico, intheUnitedStatesthecitizenscouldkeeppoliticians incheckandget

ridofoneswhowouldusetheirofficestoenrichthemselvesorcreatemonopoliesfortheir

cronies. ... The broad distribution of political rights in theUnited States, especiallywhen

compared toMexico, guaranteed equal access to finance and loans.” Our indicator of the

quality of economic institutions is the sum of three ICRG variables, namely bureaucratic

quality,corruptionandlawandorder(takingdifferencesinscalingoftheseindicatorsinto

account)whereahighernumberindicatesbetterquality.

18Duetodataavailabilitysomevariablesthathavebeensuggestedtoberelatedtoincomeinequality,suchastechnology(seee.g.Dabla‐Norrisetal.,2015),couldnotbeincluded.

16

4.Mainresults

Tables2and3presenttheresultswhereweproceedasfollows.First,weshowtheresults

whenwedonot include control variables.Asour three financemeasuresmaybe related

(e.g. more financial development may lead to more banking crises and a low level of

financial development may be an incentive for countries to introduce financial

liberalization),we first showsimplebivariate regressionsbefore includingallour finance

measures. In the next step we add the interactions outlined above. To interpret the

interactioneffects,weusegraphsassuggestedbyBramboretal.(2006).19Finally,weadd

controlvariablesinTable2thatturnouttobesignificant(inTable3weincludethesame

controls).InTable2themeasureforfinancialliberalizationbasedonAbiadetal.(2008)is

used, while in Table 3 financial liberalization is proxied by the index based on several

components of the Fraser Institute’s economic freedom index, which captures more

dimensionsofthefinancialsystemthantheindexofAbiadetal.(2010).

[Tables2and3here]

In the first three columns of Tables 2 and 3 the financial sector variables are

included separately, while column (4) shows the results when all finance measures are

included. In the regressions in these columns we do not include interaction terms and

control variables. The results suggest that financial development, financial liberalization

andbankingcrisesincreaseincomeinequality,alsowhentheyareincludedsimultaneously.

Nextweturntotheinteractionoffinancialliberalizationandfinancialdevelopment

toexaminewhetherfinancialdevelopmentconditionstheimpactoffinancialliberalization

on income inequality, as suggested by Bumann and Lensink (2016). The line in Figure 4

shows the marginal impact of financial liberalization on income inequality for different

levelsoffinancialdevelopment.Thewhiskersshowtheconfidencebandandthegreybars

showthedistributionoftheobservations.Thegraphsarebasedontheestimatesreported

in column (5) of both tables. The graphs in Figure 4 suggest that the impact of financial

liberalization is conditioned by the level of financial development: the positive impact of19Moststudiesdiscussedinsection2thatconsiderinteractionsdrawconclusionsonthebasisofthesignificanceoftheinteractionterm,whichgenerallyisnottheproperwaytodealwithinteractionsasshownbyBramboretal.(2006).

17

financial liberalization on the Gini coefficient is higher when financial development is

higher. This conclusion holds for bothmeasures of financial liberalization.20Adding time

fixedeffectsdoesnotchangeourconclusion(notshown;resultsavailableonrequest).So

these resultsdo not support thepredictionofBumannandLensink (2015) that financial

liberalizationwilldecreaseincomeinequalityathighlevelsoffinancialdevelopment.

[Figure4here]

In thenextstepweconsider institutionalquality.We firstaddourproxies for the

qualityofpoliticalandeconomic institutionstothemodelshownincolumn(4).Including

thesevariablesmayshedsomelightontherelevanceofapotentialcriticismofourresults,

namely that inequalityand financialdevelopmentarebothdrivenby institutional factors.

For instance,according toClaessensandPerotti (2007,p.749), “economic inequalityand

(financial) underdevelopment are jointly determined by institutional factorswhich cause

unequal access topoliticalandcontractual rights.” If true,addingproxies for institutional

qualityshouldaffectourresults.Itturnsoutthatdemocraticaccountabilityissignificantin

contrasttoourproxyforthequalityofeconomicinstitutionswhichisthereforenotshown

incolumn(6)ofTables2and3.Ourresultssuggestthatbetterpoliticalinstitutionsreduce

income inequality. Importantly, adding the quality of institutions does not change our

previousfindingthatfinanceincreasesincomeinequality.

Figure5showsthemarginaleffectsoffinancialliberalizationonincomeinequality

fordifferent levelsofdemocraticaccountability.Thegraphs arebasedon the regressions

shown incolumn(7)ofTables2and3.Theysuggest that thepositive impactof financial

liberalizationontheGinicoefficientishigherincountrieswithahigherqualityofpolitical

institutions. In fact,at low levelsofdemocraticaccountability financial liberalizationdoes

not significantly affect income inequality. In these regressions we do not include the

20We have also examined the interaction of financial development and the Chin‐Ito index forfinancialopenness.Kuniedaetal.(2014)arguethattherelationshipbetweenfinancialdevelopmentand income inequality is conditioned by financial openness. Their evidence, based on a sample ofmore than 100 countries for the period 1985‐2009, suggests that in financially open countries(wherefinancialopennessiscomputedfromthedatasetofLaneandMilesi‐Ferretti,2007),financialdevelopment (measuredasprivate credit toGDP) increases income inequality,while in financiallyclosed economies financial development decreases income inequality. Our results (available onrequest)donotprovideevidenceforthisview.

18

interaction between financial liberalization and financial development as financial

development has been shown to be dependent on institutional quality (see e.g. Law and

Azman‐Saini,2012).

Figure 6 presents the marginal effects of financial development on income

inequality for different levels of democratic accountability. The graphs are based on the

regressions shown in column(8)ofTables2and3.Theydonotprovidestrongevidence

thattheimpactoffinancialdevelopmentonincomeinequalityisconditionedbythequality

ofpoliticalinstitutions,incontrasttothepredictionofRajanandZingales(2003)thatunder

high‐qualityinstitutionsfinancialdevelopmentwillreduceinequality.

Theinteractionsofourfinancevariablesandourproxyforthequalityofeconomic

institutionsdonotsuggestthattheimpactoffinanceonincomeinequalityisconditionedby

the quality of economic institutions. For instance, Figure A1 in the Appendix shows the

marginaleffectsof financial liberalizationon theGinicoefficient fordifferent levelsof the

qualityofeconomicinstitutions.Althoughmostlysignificantlypositive,themarginaleffects

of financial liberalization on inequality for different levels of institutional quality are not

significantlydifferentfordifferentvaluesofinstitutionalquality(thewhiskersoverlap).

[Figures5and6here]

The next column in both tables shows the results when we add economic

globalization to themodel shown in column(7)ofTables2and3.Assaid,weconsidera

long listofpotentialcontrols,butmostofthemarenotsignificant. Inlinewithfindingsof

Sturm and de Haan (2015), globalization turns out to be significant in Tables 2 and 3

(column9).Addingcontrolsdoesnotchangeourconclusionsasshownbythemarginalplot

graphs(availableonrequest).

5. Sensitivity analysis

Inthissectionwepresenttheoutcomesofseveralsensitivityteststhathavetwopurposes.

First, as our results deviate from those of several previous studies,we examine towhat

extentour findings changewhendifferent empirical set‐ups areused. Second,we further

analyzewhetherourresultsarerobustforendogeneity,whichisakeyissueinthistypeof

19

analysis.

5.1Randomeffectsmodels

So far,our resultsarebasedonpanel fixedeffectsmodels. In this sectionwepresent the

outcomesofrandomeffectsmodelsfollowingClarkeetal.(2006)whouserandomeffects

arguing that using fixed effects takes away much (cross‐country) variation. Since the

Hausmantestsoftendonotclearlyindicatethatfixedeffectsneedtobeused,itmakessense

toalsoestimaterandomeffectsmodels.Thishasanadditionaladvantage,namelythatwe

can followseveralpreviouspapers(Clarkeetal.,2006;Kappel,2010;Kaniedaetal.2014

and Law et al., 2014) and use legal origin dummies as instruments for financial

development.AccordingtoLaPortaetal.(1997,1998),theintroductionofcommonorcivil

law into a country via conquest or colonization not only affected the legal rules but also

institutions.Forinstance,theprotectionofpropertyrightsincommonlawcountries,which

impacts thedevelopmentof financialmarkets, is stronger thanthat incivil lawcountries,

notably incountrieswithFrenchcivil law.Therefore, legalorigindummiesare frequently

usedasinstrumentalvariables(cf.AcemogluandJohnson,2005).

Table4showstheoutcomes.Columns(1)‐(4)presenttheresultswhenweusethe

measureforfinancialliberalizationbasedonthedataofAbiadetal.,whilecolumns(5)‐(8)

contain the results for the financial liberalization measure based on components of the

economicfreedomindex.

Columns(1)and(5)showtheresultswhenweestimatethemodelshownincolumn

(5) of Tables 2 and 3 which includes our finance variables and the interaction between

financial liberalizationandfinancialdevelopmentallowingforrandomeffects. Itturnsout

that the results are very similar. Next, in columns (3) and (7) we include democratic

accountability in the model containing our three finance measures together with its

interactionwithfinancial liberalization(cf.column(7) inTables2and3).Likebefore,the

results suggest that finance increases inequality, while institutional quality decreases

inequality. In countries where democratic accountability is high, the effect of financial

liberalization turns significantly positive. Hence, moving to a random effects framework

doesnotleadtodifferentresults.

Finally,columns(2),(4),(6)and(8)showtheIVresults.Incolumns(2)and(4)our

measuresforfinancialdevelopmentandfinancialliberalizationaretakenupandallowedto

20

interact.(Thiscorrespondstothespecificationincolumn(5)ofTables2and3).Incolumns

(4) and (8) the interaction between the quality of political institutions and financial

liberalization is included.(Thiscorresponds to thespecification incolumn(7)ofTables2

and3).Theoutcomessuggestthatinstrumentingfinancialdevelopmentbylegalorigindoes

notleadtodifferentoutcomes(seealsoFigureA2intheAppendix).

[Table4here]

5.2Cross‐countryregressions

Next, we present cross‐country regressions results in Table 5. Even though we feel that

panelmodelsaremostappropriateforourpurpose,wewanttocheckwhetherourresults

aredifferentwhenwefocusoncross‐countrydifferencesinincomeinequalityratherthan

within‐country income inequality. We only show the outcomes for the financial

liberalization measure based on the data of Abiad et al., as this is the variable used in

previous studies. We use the specification with the three finance variables, democratic

accountability and the interaction between financial liberalization and democratic

accountability for different cross‐sections (1991‐95, 1991‐2000, 1991‐2005, 1996‐2000,

1996‐2005,and1996‐2010).This corresponds to column(7) inTables2and3.The final

three columns show the outcomes in casewe again instrument financial developmentby

legal origin using the latter time periods. The results for banking crises and financial

liberalizationarebroadly in linewithour findingsbasedonpanel estimates,butwenow

find some evidence that financial development reduces income inequality (although the

estimatedcoefficientisnotsignificantinmostregressions).Thissuggeststhatourfocuson

within‐country income inequality explains to some extent the difference between the

resultsofourstudyfortheimpactoffinancialdevelopmentonincomeinequalityandthose

of previous studies focusing on cross‐country income inequality. Another difference

betweenthepanelandthecross‐countryregressionsisthatthecoefficientofthequalityof

politicalinstitutionsisneversignificantinthelatter.

[Table5here]

21

5.3OECDcountries

In this sectionwe report the resultswhenwe estimate somemodels forOECD countries

only.Table6showsfixedeffectspanelregressionsforthespecificationsshownincolumns

(4),(5)and(7)ofTables2and3.Ourprioristhattheinteractionswillnotbesignificant,as

the countries in this subsample aremuchmorehomogeneouswhen it comes to financial

developmentandinstitutionalqualitythanisthecaseinourfullsample.Thisindeedturns

out to be the case. Still, ourmain result that finance increases income inequality is also

confirmed for OECD countries, also when we use our alternative measure for financial

liberalization(lastthreecolumnsofTable6).

[Table6here]

6. Conclusion

Our results suggest that financialdevelopment, financial liberalization andbanking crises

increase income inequality. Inaddition, the impactof financial liberalizationon inequality

seems tobe conditionedby the levelof financialdevelopmentand thequalityofpolitical

institutions. Our findings are in contrast to several previous studies that examined the

relationshipbetweenfinancialdevelopmentandincomeinequality.

As explained in section 2, theory is not clear whether financial developmentwill

increase or decrease income inequality. Our results suggest that financial development

increasesinequality,whichisinlinewiththemodelofGreenwoodandJovanovic(1990).It

is important, however, to stress that our results donot imply that financial development

and financial liberalization are necessarily bad for the poor. There is a large literature

showingthatfinanceplaysapositiveroleinpromotingeconomicdevelopment(atleastup

to a point),21whichwill benefit the poor. An interesting avenue for future research is to

21Somerecent studies suggest that this relationshipmaybenon‐linear.For instance,Arcandetal.(2012)reportthatatintermediatelevelsoffinancialdepth,thereisapositiverelationshipbetweenthe size of the financial system and economic growth, but at high levels of financial depth, morefinanceisassociatedwithlessgrowth.Infact,themarginaleffectoffinancialdepthonoutputgrowthbecomes negative when credit to the private sector reaches 80‐100 per cent of GDP. Likewise,Cecchetti and Kharroubi (2012) report that financial development has a non‐linear impact onaggregateproductivitygrowth.Basedonasampleofdevelopedandemergingeconomies,theyshowthatthelevelof financialdevelopmentisgoodonlyuptoapoint,afterwhichitbecomesadragon

22

modeltheeffectsoffinancialdevelopmentandfinancialliberalizationonincomeinequality

andeconomicgrowthsimultaneously.

Our finding that financialdevelopmenton income inequality isnotconditionedby

democraticaccountabilityisincontrasttothepredictionofRajanandZingales(2003)that

underhigh‐quality institutions financialdevelopmentwill reduce inequality.However,we

find evidence that the impact of financial liberalization is conditioned by the quality of

politicalinstitutions.OurfindingsdoalsonotsupportthetheoreticalpredictionofBumann

and Lensink (2016) that financial liberalization will improve income distribution in

countrieswhere financialdepth ishigh.Tothecontrary,ourresultssuggest that financial

developmentenhancestheincomeinequalityincreasingeffectoffinancialliberalization.To

explainthisfindingisbeyondthescopeofthecurrentpaperandisleftforfutureresearch.22

Finally, we like to stress that our results are based on Gini coefficients for gross

income, thereby ignoring (onpurpose)government redistributionpolicies.An interesting

issue for future research is to examine whether countries that have higher income

inequality due to finance, have decided to redress this inequality by more income

redistribution. Likewise, itwould be interesting to examinewhether our results hold for

other measures for income inequality. This requires, however, that such data become

availableforalargersetofcountriesthaniscurrentlythecase.

Acknowledgements

TheviewsexpressedarethoseoftheauthorsanddonotnecessarilyreflectthoseofDNB.

We like to thank participants in the DNB Annual Research Conference (November 20,

2015), research seminars at Deakin University (January 20, 2016), the National Bank of

Serbia (February 26, 2016), IdEP, Lugano (April 12, 2016), and ISEG, Lisbon (June 23,

2016), as well as the European Public Choice conference (March, 30‐April 2, 2016), the

SUERFconference“Centralbankingandmonetarypolicy:Whichwillbethenewnormal?”

growth.22 But their model can be easily be adjusted and made in line with our results by allowing high-income agents to have a higher saving rate. That would make them benefit more from the increased loan demand – that is more pronounced in financially developed countries – that is caused by financial liberalization than those with lower income levels.

23

inMilan(April14,2016),theCESifoVeniceSummerInstitute(July20,2016),especiallyour

discussantStephanieArmbruster, theSilvaplanaWorkshoponPoliticalEconomy(July31,

2016)andtheVfS‐AnnualConference(September5,2016)fortheircommentsonprevious

versionsofthispaper.

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Tables

Table1.SummarystatisticsofdifferentGinicoefficients–SWIIDvs.WIID

Variable   Obs Mean St. Dev. Min Max   1 2 3 4

1 Gross Gini (SWIID) 530 45.37 7.26 22.66 69.85 1

2 Net Gini (SWIID) 530 38.20 9.45 19.43 66.20 0.70 1

3 Gini (WIID) 335 39.26 9.72 20.10 74.30 0.63 0.89 1

4 Gini 5 years avg. (WIID) 184 36.98 8.92 21.32 58.40 0.52 0.90 0.97 1

Correlation with

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Table2.Financeandincomeinequality:panelestimates(dependentvariable:Ginicoefficient;Abiadetal.dataforfinancialliberalization)

Table3.Financeandincomeinequality:panelestimates(dependentvariable:Ginicoefficient;economicfreedomdataforfinancialliberalization)

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Table4.RandomeffectsGLSandG2SLSestimates

Table5.Cross‐countryregressions

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES FD +IV PI +IV FD +IV PI +IV

Start of a Systemic Banking Crisis during t‐7 and t‐3 1.012** 0.954*** 1.017*** 1.023*** 1.436*** 1.221*** 1.010*** 1.000***

(2.513) (2.687) (2.862) (3.559) (3.441) (2.979) (2.720) (3.098)

Domestic credit to private sector (% of GDP) ‐0.0188 ‐0.0872* 0.0283*** 0.0124 ‐0.0358 ‐0.138** 0.0277*** 0.00508

(‐0.578) (‐1.790) (3.426) (0.560) (‐0.900) (‐2.362) (3.613) (0.191)

Financial liberalisation 0.0338 ‐0.00983 ‐0.109 ‐0.179 ‐0.0401 ‐0.0317 ‐0.618* ‐0.712**

(0.455) (‐0.137) (‐0.924) (‐1.565) (‐0.180) (‐0.128) (‐1.860) (‐2.374)

c.domcredgdp#c.finlib 0.00391** 0.00708*** 0.00919** 0.0167***

(2.202) (3.688) (2.087) (2.696)

ICRG: Democratic Accountability ‐1.456*** ‐1.706*** ‐2.020***‐2.236***

(‐3.092) (‐3.569) (‐3.257) (‐4.265)

c.democ#c.finlib 0.0817***0.105*** 0.217*** 0.258***

(2.722) (3.640) (2.831) (3.987)

Observations 426 426 345 345 518 518 410 410

Number of cntid 89 89 86 86 121 121 110 110

F‐test on domcredgdp (p‐value) 8.57e‐08 3.80e‐06

F‐test on finlib (p‐value) 0.000673 0.000638 4.49e‐05 1.77e‐05 0.00761 0.00141 0.00187 0.000113

F‐test on democ (p‐value) 0.00836 0.000754 0.00436 9.09e‐05

Notes: Country‐random effects are included. Standard errors are clustered at the country level in columns (1), (3), (5) and (7). In the "+IV" 

columns dom. credit is instrumented using legal origin dummies and bootstrapped standard errors are shown.*** p<0.01, ** p<0.05, * p<0.1.

Abiad et al. index (corrected) Avg.of EFW‐areas 3D, 4C, 4D and 5A

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Table6.RegressionsincludingonlyOECDcountries

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Figures

Figure1.Financialdevelopment(credit/GDP)andincomeinequality(Ginicoefficients)

Figure2.Financialliberalization(Abiadetal.measure)andincomeinequality(Ginicoefficients)

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Figure3.Financialliberalization(economicfreedommeasure)andincomeinequality(Ginicoefficients)

Figure4.Marginalimpactoffinancialliberalizationonincomeinequalityfordifferentlevelsoffinancialdevelopment

Figure5.Marginalimpactoffinancialliberalizationonincomeinequalityfordifferentlevelsofdemocraticaccountability

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Figure6.Marginalimpactoffinancialdevelopmentonincomeinequalityfordifferentlevelsofdemocraticaccountability

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Appendix

TableA1.Countriesincluded

34

TableA2.Variables:Descriptionandsources

35

TableA3.Summarystatistics

36

TableA4.Correlationmatrix

FigureA1.MarginaleffectsoffinancialdevelopmentontheGinicoefficientfordifferentvaluesofthequalityofeconomicinstitutions

Table2–finreform_cor Table3 – ffw_avg

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FigureA2.MarginaleffectsoffinancialliberalizationontheGinicoefficientfordifferentvaluesoffinancialdevelopmentandpoliticalinstitutionalqualityestimatedwithG2SLS

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